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---
language:
- ar
metrics:
- Accuracy
- F1_score
- BLEU
library_name: transformers
pipeline_tag: text2text-generation
tags:
- t5
- text2text-generation
- seq2seq
- Classification and Generation
- Classification
- Generation
- ArabicT5
- Text Classification
- Text2Text Generation
widget:
- example_title: "الرياضة"
- text: >
خسارة مدوية لليفربول امام تولوز وفوز كبير لبيتيس
---
# ArabicT5: Classification and Generation of Arabic News
- The model is under trial
# The number in the generated text represents the category of the news, as shown below:
category_mapping = {
'Political':1,
'Economy':2,
'Health':3,
'Sport':4,
'Culture':5,
'Technology':6,
'Art':7,
'Accidents':8
}
# Example usage
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline
model_name="Hezam/ArabicT5-news-classification-generation-45GB-base"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
generation_pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)
text = " خسارة مدوية لليفربول امام تولوز وفوز كبير لبيتيس"
output= generation_pipeline(text,
num_beams=10,
max_length=512,
top_p=0.9,
repetition_penalty = 3.0,
no_repeat_ngram_size = 3)[0]["generated_text"]
print(output)
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